In this kind of recommendation system, pertinent items are displayed based on the content of user-searched items. The product attribute or tag that the user likes is referred to as content in this context. This kind of system tags products with specific keywords, attempts to understand the user’s needs by searching its database, and then attempts to recommend various products in line with those needs.
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Let’s use the movie recommendation system as an example. In this system, each movie is assigned a genre, which is referred to as a tag or attribute in the case mentioned above. Let’s say that when user A first enters the system, no information about the user is available. In the beginning, the system either tries to recommend popular movies to users or tries to gather information about the user by having the user fill out a form.
Advantage
- After some time, users may have rated certain films. For example, action films may have received a good rating, while anime films may have received a poor rating. Therefore, this system suggests action movies to users.
- Since recommendations are tailored to a single user, Advantage Model doesn’t require data from other users.
- It makes scaling to a large number of users simpler. The model can identify the user’s specific interests and recommend products that only a small number of other users would find interesting.
Disadvantage
- To some extent, feature representation of items is hand-engineered; this technology requires extensive domain knowledge.
- Only based on a user’s current interests can the model make recommendations. In other words, the model’s capacity to build on the user’s current interests is constrained.
